There are many situations in which data from sensor and other systems is collected for event monitoring and prediction. For example, an entity with commercial refrigeration units in multiple facilities monitors data from sensors in those units to detect malfunction (e.g., failure of a refrigeration component) or predict a need for service (e.g., a particular refrigerator model unit should have its fan blower motor replaced when the sensor detects the occurrence of a signature temperature fluctuation to avoid total unit failure and loss of product). The volume of data for this monitoring and predicting can be significant, considering each sensor may detect data each second (or more often), and there may be hundreds or thousands of sensors in each of hundreds or thousands of locations.
A variety of data processing engines (DPEs) capable of processing this volume of data are commercially available, with new DPEs providing enhanced performance or new features becoming available all the time. Conventionally, engine-specific instructions for the desired monitoring and predicting tasks must be written to enable a specific DPE to operate. This is time-consuming, particularly when a new DPE becomes available and existing instructions must be manually rewritten to enable a migration from the current DPE to the new DPE.